Incorporating usage information into average-clicks algorithm

Kalyan Beemanapalli, Ramya Rangarajan, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


A number of methods exists that measure the distance between two web pages. Average-Clicks is a new measure of distance between web pages which fits user's intuition of distance better than the traditional measure of clicks between two pages. Average-Clicks however assumes that the probability of the user following any link on a web page is the same and gives equal weights to each of the out-going links. In our method "Usage Aware Average-Clicks" we have taken the user's browsing behavior into account and assigned different weights to different links on a particular page based on how frequently users follow a particular link. Thus, Usage Aware Average-Clicks is an extension to the Average-Clicks Algorithm where the static web link structure graph is combined with the dynamic Usage Graph (built using the information available from the web logs) to assign different weights to links on a web page and hence capture the user's intuition of distance more accurately. A new distance metric has been designed using this methodology and used to improve the efficiency of a web recommendation engine.

Original languageEnglish (US)
Title of host publicationAdvances in Web Mining and Web Usage Analysis - 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006, Revised Papers
PublisherSpringer Verlag
Number of pages15
ISBN (Print)354077484X, 9783540774846
StatePublished - 2007
Event8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006 - Philadelphia, PA, United States
Duration: Aug 20 2006Aug 20 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4811 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006
Country/TerritoryUnited States
CityPhiladelphia, PA


  • Link analysis
  • Recommendation engines
  • Web mining
  • Web usage analysis


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